import torch import torch.nn.functional as F import torch.distributions as dists import transformers from transformers import AutoTokenizer from peft import PeftModel import numpy as np import random import time import os from typing import List, Dict, Optional, Tuple, Iterator, Set import gradio as gr import spaces # ← 新增：导入 spaces 模块 # Suppress some Hugging Face warnings os.environ["TOKENIZERS_PARALLELISM"] = "false" # Import necessary model classes from the local directory from model_cache.llada.modeling_llada import LLaDAModelLM from model_cache.llada.configuration_llada import LLaDAConfig # --- Helper Functions (Unchanged) --- def set_seed(seed): torch.manual_seed(seed); random.seed(seed); np.random.seed(seed); if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed); torch.backends.cudnn.deterministic = True; torch.backends.cudnn.benchmark = False def create_full_block_attention_mask(prompt_length, max_length, block_size, device=None, dtype=None): if dtype is None: dtype = torch.bfloat16 attention_mask = torch.full((1, 1, max_length, max_length), -torch.inf, device=device, dtype=dtype) attention_mask[:, :, :prompt_length, :prompt_length] = 0 remaining_length = max_length - prompt_length num_blocks = (remaining_length + block_size - 1) // block_size for b in range(num_blocks): block_start = prompt_length + b * block_size; block_end = min(prompt_length + (b + 1) * block_size, max_length) attention_mask[:, :, block_start:block_end, :prompt_length] = 0 for prev_b in range(b): prev_start = prompt_length + prev_b * block_size; prev_end = min(prompt_length + (prev_b + 1) * block_size, max_length) attention_mask[:, :, block_start:block_end, prev_start:prev_end] = 0 attention_mask[:, :, block_start:block_end, block_start:block_end] = 0 return attention_mask def extract_attention_mask(full_mask, start_pos, input_length, cache_length): end_pos = start_pos + input_length; total_length = cache_length + input_length extracted_mask = torch.full((1, 1, input_length, total_length), -torch.inf, device=full_mask.device, dtype=full_mask.dtype) extracted_mask[:, :, :, :cache_length] = full_mask[:, :, start_pos:end_pos, :cache_length] extracted_mask[:, :, :, cache_length:] = full_mask[:, :, start_pos:end_pos, start_pos:end_pos] return extracted_mask def top_p_logits(logits, top_p=None): sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 mask = torch.zeros_like(logits, dtype=torch.bool, device=logits.device) mask = mask.scatter_(-1, sorted_indices, sorted_indices_to_remove) logits = logits.masked_fill(mask, torch.finfo(logits.dtype).min) return logits def top_k_logits(logits, top_k=None): top_k = min(top_k, logits.size(-1)) indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits = logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min) return logits def sample_tokens(logits, temperature=0.0, top_p=None, top_k=None, margin_confidence=False, neg_entropy=False): if temperature > 0: logits = logits / temperature if top_p is not None and top_p < 1: logits = top_p_logits(logits, top_p) if top_k is not None: logits = top_k_logits(logits, top_k) probs = torch.softmax(logits, dim=-1) if temperature > 0: try: x0 = dists.Categorical(probs=probs).sample() initial_confidence = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1) except: initial_confidence, x0 = probs.max(dim=-1) else: initial_confidence, x0 = probs.max(dim=-1) confidence = initial_confidence.clone() if margin_confidence: sorted_probs, _ = torch.sort(probs, dim=-1, descending=True) confidence = sorted_probs[:, 0] - sorted_probs[:, 1] if neg_entropy: epsilon = 1e-10 confidence = torch.sum(probs * torch.log(probs + epsilon), dim=-1) return confidence, x0, initial_confidence class DreamLoRAInference: CSS = """ /* Fixed height, scrollable visualization container */ #viz-container { height: 500px; overflow-y: auto !important; border: 1px solid #E5E7EB; border-radius: 8px; padding: 10px; position: relative; } .block-container { display: inline-block; border: 2px solid transparent; border-radius: 8px; padding: 5px; margin: 4px 0; transition: border-color 0.3s, box-shadow 0.3s; } .block-updating { border-color: #FF4500 !important; box-shadow: 0 0 8px rgba(255, 69, 0, 0.7); } .token { padding: 2px 4px; margin: 2px; border-radius: 4px; display: inline-block; line-height: 1.4; font-family: monospace; } .token.prompt { background-color: #E5E7EB; color: #4B5563; } .token.gen-0 { background-color: #DBEAFE; color: #1E40AF; } /* Blue */ .token.gen-1 { background-color: #D1FAE5; color: #065F46; } /* Green */ .token.gen-2 { background-color: #FEF3C7; color: #92400E; } /* Yellow */ .token.gen-3 { background-color: #FEE2E2; color: #991B1B; } /* Red */ .token.gen-4 { background-color: #E0E7FF; color: #3730A3; } /* Indigo */ .token.gen-5 { background-color: #F3E8FF; color: #6B21A8; } /* Purple */ .token.mask { background-color: #F3F4F6; color: #9CA3AF; border: 1px dashed #D1D5DB; } /* Independent status box styles */ #status-container { height: 300px; overflow-y: auto !important; margin-top: 10px; padding: 15px; border: 1px solid #E5E7EB; border-radius: 8px; background-color: #F9FAFB; position: relative; } #status-container h4 { margin-top: 0; } .status-line { font-family: monospace; font-size: 13px; margin-bottom: 5px; margin-top: 5px; padding: 2px 4px; border-radius: 3px;} #stats-output { padding: 15px; border: 1px solid #10B981; border-radius: 8px; background-color: #F0FDF4; margin-top: 10px; } /* Scroll anchor */ .scroll-anchor { height: 1px; width: 100%; } /* Force scrollbar styles */ #viz-container::-webkit-scrollbar, #status-container::-webkit-scrollbar { width: 10px !important; background-color: #f5f5f5 !important; } #viz-container::-webkit-scrollbar-thumb, #status-container::-webkit-scrollbar-thumb { background-color: #888 !important; border-radius: 5px !important; } #viz-container::-webkit-scrollbar-track, #status-container::-webkit-scrollbar-track { background-color: #f5f5f5 !important; border-radius: 5px !important; } /* Column height alignment */ .left-column, .right-column { display: flex; flex-direction: column; height: auto !important; min-height: 800px; } .live-text-container, .viz-status-container { display: flex; flex-direction: column; flex: 1; overflow: visible; } #live-text-output, #stats-output { margin-bottom: 20px; } /* Fix for bottom content being cut off */ .container { padding-bottom: 40px; } /* Make sure content is fully visible */ .gradio-container { overflow-y: visible !important; } /* Add padding to bottom of page */ .footer { margin-top: 30px; padding-bottom: 30px; } """ def __init__(self, **kwargs): print("Initializing DreamLoRAInference...") self.device = torch.device(kwargs.get("device", "cuda") if torch.cuda.is_available() else "cpu") self.__dict__.update(kwargs) if self.dtype == "bfloat16" and torch.cuda.is_bf16_supported(): self.target_dtype = torch.bfloat16 elif self.dtype == "float16": self.target_dtype = torch.float16 else: self.target_dtype = torch.float32 self._setup_model(self.pretrained_path, self.lora_path) print("Model and tokenizer setup complete.") def _setup_model(self, pretrained_path, lora_path): # --- MODIFICATION START --- # The arguments `trust_remote_code=True` have been removed as they are not needed here # and were causing warnings in the log. config = LLaDAConfig.from_pretrained(pretrained_path) self.model = LLaDAModelLM.from_pretrained( pretrained_path, config=config, torch_dtype=self.target_dtype, # device_map="auto" is handled by accelerate for better memory management on Spaces device_map="auto" ).eval() # THIS IS THE CRITICAL FIX: Tie the weights before loading the adapter. # This resolves the error message from the log and allows `device_map="auto"` to work correctly. # print("Tying model weights...") # self.model.tie_weights() # print("Weights tied.") # Now, load the PEFT adapter on top of the correctly configured base model self.model = PeftModel.from_pretrained(self.model, lora_path) # --- MODIFICATION END --- self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path) if self.tokenizer.pad_token is None: self.tokenizer.pad_token = self.tokenizer.eos_token def _apply_chat_template(self, prompt): chat_history = [{"role": "user", "content": prompt}] return self.tokenizer.apply_chat_template(chat_history, tokenize=False, add_generation_prompt=True) def _update_block_completion_states(self, block_states, decoded_token_threshold): for block_id in sorted(block_states.keys()): decoded_tokens = block_states[block_id]['total_masks'] - block_states[block_id]['mask_count'] if block_states[block_id]['total_masks'] > 0: decode_ratio = decoded_tokens / block_states[block_id]['total_masks'] if decode_ratio >= decoded_token_threshold: if (next_block_id := block_id + 1) in block_states: block_states[next_block_id]['is_complete'] = True # The rest of your class methods (_render_visualization_html, _render_status_html, stream_and_capture_for_gradio) # remain completely unchanged. def _render_visualization_html(self, step: int, x_t: torch.Tensor, block_states: Dict, cache_length: int, updated_block_ids: Set[int]) -> str: timestamp = int(time.time() * 1000) html_parts = [] for block_id in sorted(k for k in block_states.keys() if k > 0): # Only render generated part (block_id > 0) state = block_states[block_id] container_classes = ["block-container"] if block_id in updated_block_ids: container_classes.append("block-updating") html_parts.append(f'
') block_tokens = x_t[0, state['start_pos']:state['end_pos']] for token_id in block_tokens: token_id_int = token_id.item() token_classes = ["token"] if token_id_int == self.mask_token_id: token_str = '░'; token_classes.append("mask") else: token_str = self.tokenizer.decode([token_id_int], skip_special_tokens=False) token_str = token_str.replace('&', '&').replace('<', '<').replace('>', '>') token_classes.append(f"gen-{(block_id - 1) % 6}") html_parts.append(f'{token_str}') html_parts.append('
') html_parts.append(f'
') complete_html = f"""
{''.join(html_parts)}
""" return complete_html def _render_status_html(self, step: int, block_states: Dict, cache_length: int) -> str: timestamp = int(time.time() * 1000) html_parts = [] html_parts.append(f'

Generation Block Status (Step: {step}, Cache Length: {cache_length})

') for block_id in [k for k in sorted(block_states.keys()) if k > 0]: state = block_states[block_id] block_type = f"Block {block_id}" masks_filled = state['total_masks'] - state['mask_count'] color_class = f"gen-{(block_id - 1) % 6}" status_line = f'{block_type.ljust(8)}: Pos=[{str(state["start_pos"]).rjust(4)}:{str(state["end_pos"]).ljust(4)}] | State=\'{state["state"].ljust(8)}\' | Filled={str(masks_filled).rjust(2)}/{state["total_masks"]}' html_parts.append(f'

{status_line}

') html_parts.append(f'
') complete_html = f"""
{''.join(html_parts)}
""" return complete_html @spaces.GPU # ← 新增：关键修复 - 添加 GPU 装饰器 @torch.inference_mode() def stream_and_capture_for_gradio( self, prompt_text: str, max_new_tokens: int, block_size: int, block_add_threshold: float, decoded_token_threshold: float, skip_threshold: float ) -> Iterator[Tuple[str, List[Tuple[str, str]], str, str, str]]: start_time = time.time() captured_frames: List[Tuple[str, str]] = [] # Initialization input_ids = self.tokenizer(self._apply_chat_template(prompt_text), return_tensors="pt").input_ids.to(self.model.device) prompt_length = input_ids.shape[1] full_attention_mask = create_full_block_attention_mask(prompt_length, self.max_length, block_size, self.model.device, self.target_dtype) x_t = input_ids block_states = {0: {'start_pos': 0, 'end_pos': prompt_length, 'mask_count': 0, 'total_masks': prompt_length, 'state': 'to_cache', 'is_complete': True}} past_key_values, current_blocks, step, eos_detected, cache_length = None, 0, 0, False, 0 # Capture initial state initial_viz_html = self._render_visualization_html(0, x_t, block_states, 0, set()) initial_status_html = self._render_status_html(0, x_t, block_states, 0) captured_frames.append((initial_viz_html, initial_status_html)) yield "", captured_frames, "Initializing generation process...", "Initializing visualization...", "Initializing block status..." # Main generation loop while True: step += 1 updated_block_ids: Set[int] = set() if len(block_states) - 1 < (max_new_tokens // block_size) and not eos_detected: last_block_id = max(block_states.keys()) progress = (block_states[last_block_id]['total_masks'] - block_states[last_block_id]['mask_count']) / block_states[last_block_id]['total_masks'] if block_states[last_block_id]['total_masks'] > 0 else 1.0 if progress >= block_add_threshold: new_block_id = last_block_id + 1; new_start_pos = x_t.shape[1] if new_start_pos + block_size <= self.max_length: x_t = torch.cat([x_t, torch.full((1, block_size), self.mask_token_id, device=self.model.device, dtype=torch.long)], dim=1) block_states[new_block_id] = {'start_pos': new_start_pos, 'end_pos': new_start_pos + block_size, 'mask_count': block_size, 'total_masks': block_size, 'state': 'active', 'is_complete': False} current_blocks += 1 self._update_block_completion_states(block_states, decoded_token_threshold) if (x_t == self.mask_token_id).sum() == 0 and current_blocks == 0: break blocks_to_cache = [bid for bid, state in block_states.items() if state['state'] == 'to_cache'] update_kvcache = 0 if blocks_to_cache: start_pos, end_pos = block_states[min(blocks_to_cache)]['start_pos'], block_states[max(blocks_to_cache)]['end_pos'] update_kvcache = end_pos - start_pos; input_seq, process_start_pos = x_t[:, start_pos:], start_pos else: active_blocks = [bid for bid, state in block_states.items() if state['state'] == 'active' and state['start_pos'] >= cache_length] if not active_blocks: break start_pos = min(block_states[bid]['start_pos'] for bid in active_blocks); input_seq, process_start_pos = x_t[:, start_pos:], start_pos if input_seq.shape[1] == 0: break attention_mask = extract_attention_mask(full_attention_mask, process_start_pos, input_seq.shape[1], cache_length) outputs = self.model(input_seq, attention_bias=attention_mask, past_key_values=past_key_values, use_cache=True, update_kvcache=update_kvcache + cache_length) if update_kvcache > 0: past_key_values = outputs.past_key_values for bid in blocks_to_cache: block_states[bid]['state'] = 'in_cache' blocks_to_deactivate = [] for block_id, state in block_states.items(): if state['state'] != 'active': continue block_mask_locs = (x_t[0, state['start_pos']:state['end_pos']] == self.mask_token_id).nonzero().squeeze(-1) if block_mask_locs.numel() == 0: blocks_to_deactivate.append(block_id); continue logit_offset = state['start_pos'] - process_start_pos block_mask_logits = outputs.logits[:, logit_offset + block_mask_locs, :] _, x0, initial_confidence = sample_tokens(block_mask_logits.squeeze(0), self.temperature, self.top_p, self.top_k) all_indices = (initial_confidence > skip_threshold).nonzero().squeeze(-1) if state['is_complete'] and all_indices.numel() == 0 and block_mask_logits.numel() > 0: all_indices = torch.tensor([torch.argmax(initial_confidence)], device=self.model.device) if all_indices.numel() > 0: updated_block_ids.add(block_id) positions_to_update = state['start_pos'] + block_mask_locs[all_indices] x_t[0, positions_to_update] = x0[all_indices]; state['mask_count'] -= all_indices.numel() if self.tokenizer.eos_token_id in x0[all_indices]: eos_detected = True if state['mask_count'] == 0: blocks_to_deactivate.append(block_id) for bid in blocks_to_deactivate: if block_states[bid]['state'] == 'active' and all(block_states.get(i, {}).get('state') != 'active' for i in range(bid)): block_states[bid]['state'] = 'to_cache'; current_blocks -= 1 if update_kvcache > 0: cache_length += update_kvcache generated_ids = x_t[0, prompt_length:] valid_ids = generated_ids[generated_ids != self.mask_token_id] live_text = self.tokenizer.decode(valid_ids, skip_special_tokens=True) current_viz_html = self._render_visualization_html(step, x_t, block_states, cache_length, updated_block_ids) current_status_html = self._render_status_html(step, block_states, cache_length) captured_frames.append((current_viz_html, current_status_html)) yield live_text, captured_frames, "Generating...", "Generating...", "Generating..." total_time = time.time() - start_time final_generated_ids = x_t[0, prompt_length:] eos_positions = (final_generated_ids == self.tokenizer.eos_token_id).nonzero() if eos_positions.numel() > 0: final_generated_ids = final_generated_ids[:eos_positions[0, 0] + 1] final_text = self.tokenizer.decode(final_generated_ids, skip_special_tokens=True) final_viz_html = self._render_visualization_html(step, x_t, block_states, cache_length, set()) final_status_html = self._render_status_html(step, block_states, cache_length) captured_frames.append((final_viz_html, final_status_html)) tokens_incl_eos = len(final_generated_ids) tokens_excl_eos = len(final_generated_ids[final_generated_ids != self.tokenizer.eos_token_id]) stats_text = f""" ### ✅ Generation Complete! --- - **Total time:** `{total_time:.2f} seconds` - **Tokens generated (incl. EOS):** `{tokens_incl_eos}` - **Tokens generated (excl. EOS):** `{tokens_excl_eos}` - **Tokens per second:** `{(tokens_incl_eos / total_time):.2f}` """ yield final_text, captured_frames, stats_text, "Generation complete, playback starting soon", "Generation complete, playback starting soon" # --- Gradio UI and Event Handlers --- if __name__ == "__main__": config = { "pretrained_path": "GSAI-ML/LLaDA-8B-Instruct", "lora_path": "SJTU-Deng-Lab/D2F_LLaDA_Instruct_8B_Lora", "device": "cuda", "dtype": "bfloat16", "max_length": 4096, "temperature": 0.0, "top_p": None, "top_k": None, "mask_token_id": 126336, "sampling_strategy": "default", } set_seed(42) inference_engine = DreamLoRAInference(**config) def animate_visualization(html_frames_list: List[Tuple[str, str]], delay: float) -> Iterator[Tuple[str, str]]: if not html_frames_list: yield "No visualization data captured", "No status data captured" return for viz_frame, status_frame in html_frames_list: yield viz_frame, status_frame time.sleep(delay) auto_scroll_js = """ """ with gr.Blocks(css=DreamLoRAInference.CSS, theme=gr.themes.Soft()) as demo: html_frames_state = gr.State([]) gr.Markdown("# ✨ D2F-LLaDA: Real-time Text vs. Slow-motion Visualization") gr.Markdown("Left side shows real-time streaming output. Right side plays back the decoding process visualization after generation completes.") gr.HTML(auto_scroll_js) with gr.Row(): with gr.Column(scale=2, elem_classes=["left-column"]): prompt_input = gr.Textbox(label="Enter your question", placeholder="Example: Natalia sold clips to...", lines=5) generate_button = gr.Button("🚀 Generate & Visualize", variant="primary") with gr.Group(elem_classes=["live-text-container"]): live_text_output = gr.Textbox(label="Real-time Generation Output", interactive=False, lines=25, elem_id="live-text-output") stats_output = gr.Markdown(label="Generation Statistics", elem_id="stats-output") with gr.Column(scale=3, elem_classes=["right-column"]): with gr.Accordion("⚙️ Parameter Settings", open=True): with gr.Row(): max_new_tokens_slider = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, label="Max Tokens to Generate") block_size_slider = gr.Slider(minimum=16, maximum=128, value=32, step=16, label="Block Size") with gr.Row(): block_add_thresh_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.05, label="Block Add Threshold") decoded_token_thresh_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Decoding Completion Threshold") skip_thresh_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.9, step=0.01, label="Skip Threshold") delay_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.05, label="Playback Delay (seconds)", info="Adjust visualization playback speed.") with gr.Group(elem_classes=["viz-status-container"]): visualization_output = gr.HTML(label="Generation Process Visualization", elem_id="viz-container") status_output_html = gr.HTML(label="Generation Block Status", elem_id="status-container") gr.Examples( examples=[ ["Solve the equation x² - 6x + 8 = 0. First, explain what a quadratic equation is and why it can have up to two solutions. Then solve this equation using three different methods: factoring, completing the square, and the quadratic formula. For each method, explain the mathematical reasoning behind it, show all steps in detail, and discuss when this particular method is most useful. Finally, verify your solutions by substituting them back into the original equation.", 1024, 32, 0.1, 0.55, 0.9, 0.1], ["A circular swimming pool has a diameter of 8 meters. Calculate the pool's circumference and area. First, explain the relationship between diameter, radius, circumference, and area of a circle, including the role of π in these formulas. Then perform the calculations using π ≈ 3.14159. Next, estimate how much water (in cubic meters) would be needed to fill this pool if it has a uniform depth of 1.5 meters. Finally, calculate how much it would cost to fill this pool if water costs $2.50 per cubic meter. Show all steps and include appropriate units in your answer.", 1024, 32, 0.1, 0.5, 0.9, 0.1], ["A movie theater offers a loyalty card that costs $15 and gives a 15% discount on all tickets. If a regular movie ticket costs $10, how many tickets would you need to buy to make the loyalty card worthwhile? First, explain the concept of a break-even point. Then set up an equation to find when the total cost with the card equals the total cost without the card. Solve this equation step by step, showing all your work. Finally, interpret your answer in the context of the problem.", 1024, 32, 0.1, 0.5, 0.9, 0.1], ], inputs=[ prompt_input, max_new_tokens_slider, block_size_slider, block_add_thresh_slider, decoded_token_thresh_slider, skip_thresh_slider, delay_slider ], label="Examples (Math Problems)" ) inputs_list = [ prompt_input, max_new_tokens_slider, block_size_slider, block_add_thresh_slider, decoded_token_thresh_slider, skip_thresh_slider ] generation_event = generate_button.click( fn=inference_engine.stream_and_capture_for_gradio, inputs=inputs_list, outputs=[live_text_output, html_frames_state, stats_output, visualization_output, status_output_html] ) generation_event.then( fn=animate_visualization, inputs=[html_frames_state, delay_slider], outputs=[visualization_output, status_output_html] ) demo.queue().launch()